kfbd Karadeniz Fen Bilimleri Dergisi 2564-7377 Giresun University 10.31466/kfbd.1536270 Computer Software Software Engineering (Other) Bilgisayar Yazılımı Yazılım Mühendisliği (Diğer) Examining the Effect of Text Representation Methods on Classification Performance in Sentiment Analysis Based on Machine Learning and Deep Learning Makine Öğrenmesi ve Derin Öğrenmeye Dayalı Duygu Analizinde Metin Temsil Yöntemlerinin Sınıflandırma Başarımına Etkisinin İncelenmesi https://orcid.org/0009-0000-8418-6778 Kahraman Aleyna ONDOKUZ MAYIS ÜNİVERSİTESİ https://orcid.org/0000-0002-0831-7825 Şahin Durmuş Özkan ONDOKUZ MAYIS ÜNİVERSİTESİ https://orcid.org/0009-0001-4474-9588 Bıyıklı Dilara ONDOKUZ MAYIS ÜNİVERSİTESİ https://orcid.org/0009-0003-2344-7657 Aytekin Feyzanur ONDOKUZ MAYIS ÜNİVERSİTESİ https://orcid.org/0009-0003-9715-9922 Darga Hasan Basri ONDOKUZ MAYIS ÜNİVERSİTESİ 06 15 2025 15 2 923 959 08 20 2024 03 13 2025 Copyright © 2010, The Black Sea Journal of Sciences 2010 The Black Sea Journal of Sciences

In this study, binary and multi-class classification were performed on two different datasets using various text representation methods in conjunction with multiple machine learning and deep learning algorithms. The text representation methods employed include TF-IDF, GloVe, Word2Vec, FastText, and Bag of Words. The machine learning algorithms applied were Naive Bayes, Lojistik Regresyon, Support Vector Machines, Random Forest, Artificial Neural Networks, K-Nearest Neighbors, Decision Trees, XGBoost, and LightGBM. For deep learning algorithms, Convolutional Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory were utilized. The performance of the text representation methods and algorithms was compared based on the results obtained. In the Amazon dataset, the highest accuracy among the machine learning methods was achieved by the LightGBM algorithm. Among the deep learning methods, the LSTM algorithm using TF-IDF and FastText achieved the highest accuracy. In the IMDb dataset, the Lojistik Regresyon algorithm provided the highest accuracy among the machine learning methods, while the LSTM algorithm using FastText achieved the highest accuracy among the deep learning methods.

Bu çalışmada, iki farklı veri seti üzerinde çeşitli metin temsil yöntemleri kullanılarak ikili ve üçlü sınıflandırma işlemleri gerçekleştirilmiştir. Metin temsil yöntemleri olarak TF-IDF, GloVe, Word2Vec, FastText ve Bag of Words kullanılmıştır. Makine öğrenimi algoritmalarından Naive Bayes, Lojistik Regresyon, Destek Vektör Makineleri, Rastgele Orman, Yapay Sinir Ağı, En Yakın Komşu Algoritması, Karar Ağacı, XGBoost ve LightGBM uygulanmıştır. Derin öğrenme algoritmaları olarak ise Evrişimli Sinir Ağı, Tekrarlayan Sinir Ağı ve Uzun Kısa Süreli Bellek kullanılmıştır. Elde edilen sonuçlarla, kullanılan metin temsil yöntemleri ve algoritmaların performansları karşılaştırılmıştır. Amazon veri setinde, makine öğrenimi yöntemleri arasında en yüksek doğruluk oranı LightGBM algoritması, derin öğrenme yöntemleri arasında ise TF-IDF ve FastText kullanan LSTM algoritması tarafından elde edilmiştir. IMDb veri setinde, makine öğrenimi yöntemleri arasında en yüksek doğruluk oranı Lojistik Regresyon algoritması, derin öğrenme yöntemleri arasında ise FastText kullanan LSTM algoritması tarafından elde edilmiştir.

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